The proposed method spans the subject domains of user behavioral analysis and online participation. Users on social media are able to engage in various online social activities such as posting, browsing, and social networking. Understanding and analyzing their behavior is important, particularly for service providers and researchers.
These available data on social media can be divided into four categories: (i) user's profile information, (ii) user's activity, (iii) user's network connectivity, and (iv) user's generated content. All these categories are important in analyzing the user's behavior and characteristics. In the field of social media behavioral analysis, researchers have utilized different conjunctions of these categories of data [1]}, [2]}, [3]}, [4]}. However, little attention has been given to the concept of participation inequality. More precisely, these methods do not distinguish between different types of users in terms of the amount of their created content and they commonly interpret the users' silence as a lack of engagement [5]}.
In the field of psychology, behavioral, and social science, there is a wide range of studies dedicated to extracting factors that drive user participation as well as lurking behavior in online social communities [6]}. These behavioral factors can be classified into three major categories: (1) individual-level which takes into account the intrinsic and personal characteristics of the users such as their personality traits, (2) community-level which is related to the social aspect and the nature of the online communities, and (3) environmental-level which looks into the extrinsic environmental influence such as the platform's functionalities, characteristics, and development process. In other words, with suitable and safe infrastructure, due to the users' personal characteristics as well as the reciprocity factor in OSNs, the users have the confidence to contribute to the online content as well as perceiving that their posts are useful for the community and receive by a lot of people. Moreover, these studies discuss that lurking behavior is actually normal, positive, active, and a valuable form of online behavior [7]}, [8]}. By providing an explanation of the users' silence we can design strategies to encourage posting [6]} or devise unbiased social methods [10]} such as more accurate election predictors [11]} and fact-finding algorithms [5]}.
